[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126815-en":3,"doc-seo-126815-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126815,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",7,"Healthcare","Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection","Urinary tract infections (UTIs) cause severe complications such as pyelonephritis and urosepsis and are increasingly driven by drug-resistant pathogens worldwide. Existing diagnostics like urine culture and flow cytometry involve time-consuming workflows and costly instruments. A machine learning-assisted colorimetric sensor array using recognition ligand-functionalized Fe single-atom nanozymes is presented for microorganism identification at order, genus, and species levels. The array generates microbial fingerprints and achieves >10 targets within one hour with up to 97% accuracy in 60 clinical samples, supporting rapid clinical translation.","Machine learning-assistant colorimetric sensor arrays for intelligent and rapid diagnosis of urinary tract infection  \nJianyu Yang1†, Ge Li1†, Shihong Chen2, Xiaozhi Su3, Dong Xu4, Yueming Zhai5, Yuhang Liu1, Guangxuan Hu1, Chunxian Guo1 *, Hong Bin Yang1 *, Luigi G. Occhipinti6 *, Fang Xin Hu1 *  \n1 School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China  \n2 School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China  \n3 Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China  \n4Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang 317502, China; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang 310022, China; Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital, Taizhou, Zhejiang 317502, China  \n5The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China  \n6Department of Engineering, University of Cambridge, 9 J J Thomson Avenue, Cambridge CB3 0FA, United Kingdom  \n†These authors contributed equally  \nCorresponding authors: [cxguo@usts.edu.cn](cxguo@usts.edu.cn) (C. Guo); [yanghb@mail.usts.edu.cn](yanghb@mail.usts.edu.cn) (HB.  \nYang); [hufx278@usts.edu.cn](hufx278@usts.edu.cn) (FX. Hu); [lgo23@cam.ac.uk](lgo23@cam.ac.uk) (L.G. Occhipinti)  \nKEYWORDS: colorimetric sensor array, urinary tract infections, Fe single-atom nanozyme, microorganism identification, machine learning  \nABSTRACT: Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in incidence due to the emergence of drug-resistant pathogens.  \nCurrent diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine  \nlearning-assisted colorimetric sensor array based on recognition ligand-functionalized Fe single-atom nanozymes for the identification of microorganisms at the order, genus,  \nand species levels. Colorimetric sensor arrays are built from the single-atom nanozymes (SANs) Fe 1-NC functionalized with four types of recognition ligands, generating  \nunique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more  \nthan 10 microorganisms in UTI urine samples within one hour. Diagnostic accuracy up  \nto 97% was achieved in 60 UTIs clinical samples, holding great potential for translating into clinical practice applications.  \nIntroduction  \nPathogenic microorganisms that can cause a wide range of infectious diseases posea significant threat to the public health 1. Urinary tract infections (UTIs) are common diseases caused by the invasion of pathogens in urinary tracts2. They can lead to further complications such as pyelonephritis, urosepsis, and even death3, making them among the most prevalent infection-related diseases worldwide. The main UTI-related pathogens are Gram-negative bacteria (e.g., Escherichia coli), Gram-positive bacteria (e.g., Enterococcus faecalis), and fungi (e.g., Candida albicans)4-6. The emergence of drug-resistant pathogens has significantly increased the difficulty of treating UTI7, 8 diseases in recent years. The current standard method for UTIs diagnosis is urine culture, which requires multistep protocols and is time-consuming9. Other technologies such as high-throughput sequencing and flow cytometry have also been utilized to detect microorganism in human urines 10, 11. These ","cbCaivkETedzQNU5","https://ap.wps.com/l/cbCaivkETedzQNU5","pdf",6256491,2,1,39,"English","en",105,"# Introduction\n## UTIs and clinical challenges\n## Limitations of current diagnostic methods\n## Need for multi-microorganism rapid identification\n# Sensor design and recognition strategy\n## Optical sensing arrays for microbial analysis\n## Single-atom Fe-N-C nanozymes and colorimetric response\n# Machine learning classification workflow\n## Fingerprint generation from recognition ligands\n## One-hour identification and reported accuracy","[{\"question\":\"What problem does the machine learning-assisted colorimetric sensor array address for UTIs?\",\"answer\":\"It enables rapid identification of multiple microorganisms in urine, overcoming the slow and equipment-intensive nature of conventional diagnostics like urine culture and flow cytometry.\"},{\"question\":\"How does the sensor array produce microbial identification fingerprints?\",\"answer\":\"It uses Fe1-NC single-atom nanozymes functionalized with four recognition ligands, producing distinct colorimetric patterns that reflect different microbes.\"},{\"question\":\"What performance was reported for clinical testing?\",\"answer\":\"The approach identified more than 10 microorganisms within one hour and achieved diagnostic accuracy up to 97% across 60 UTIs clinical samples.\"}]","Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection | 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problem does the machine learning-assisted colorimetric sensor array address for UTIs?","Question",{"text":76,"@type":77},"It enables rapid identification of multiple microorganisms in urine, overcoming the slow and equipment-intensive nature of conventional diagnostics like urine culture and flow cytometry.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the sensor array produce microbial identification fingerprints?",{"text":81,"@type":77},"It uses Fe1-NC single-atom nanozymes functionalized with four recognition ligands, producing distinct colorimetric patterns that reflect different microbes.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance was reported for clinical testing?",{"text":85,"@type":77},"The approach identified more than 10 microorganisms within one hour and achieved diagnostic accuracy up to 97% across 60 UTIs clinical 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